The present invention relates to a monitor apparatus using an imaging device, or in particular to an intruding object detection method and an intruding object monitor apparatus for detecting an intruding object within an imaging field of view based on an area having pixels for each of which the difference in luminance or brightness between an input image from the imaging device and a reference background image is not less than an intruding object detection threshold, or more in particular to an intruding object detection method, a detection parameter setting method and an intruding object monitor apparatus for determining the intruding object detection threshold automatically based on the input image from the imaging device.
In recent years, an intruding object detection apparatus using an imaging device such as a camera as image input means has often been operated not manually by a monitor operator but automatically by detecting an intruding object within a monitor view of field or automatically checking the category of the object and issuing a predetermined announcement or alarm. An example is described in JP-A-7-79429.
In order to realize the system described above, the first requirement is the process for detecting a predetermined intruding object from an input image obtained by image input means such as a camera. In a method of realizing such a process, an input image is compared with a reference background image (i.e. an image not containing an object to be detected) to determine a difference for each pixel, and an area having a large difference is extracted as an object. This method is called a subtraction method and has been widely used.
The process of the subtraction method will be explained with reference to FIG. 12, which is a diagram showing the principle of the object detect operation according to the subtraction method. Reference numeral 1201 designates an input image, numeral 1202 a reference background image, numeral 1203 a difference image, numeral 1204 a binarized image of the difference image 1203, numeral 1205 a subtractor, numeral 1206 a binarizer, numeral 1207 an area of an human-shaped object contained in the input image 1201, numeral 1208 a differential area having a difference in the difference image 1203, and numeral 1209 a binarized area extracted from the binarized image 1204.
In FIG. 12, the subtractor 1205 calculates the difference of the brightness value between the input image 1201 and the reference background image 1202 for each pixel and outputs the difference image 1203. The binarizer 1206 produces a binarized image 1204 assuming for each pixel that pixels having the pixel value (difference value) in the difference image 1203 less than a predetermined threshold Th (difference value<Th) have the pixel value “0” and pixels having the pixel value of the difference image 1203 not less than the threshold Th (difference value≧Th) have the pixel value “255” (the pixel value of each pixel is calculated in 8 bits). The threshold Th is set to, say, “20”.
As a result, the human-shaped object 1207 contained in the input image 1201 is processed in such a manner that an area 1208 having developed a difference is calculated by the subtractor 1205, and a cluster of pixels constituting an image 1209 having the brightness value of “255” as produced from the binarizer 1206 is detected as an intruding object.
An example of the intruding object recognition method using this method will be explained with reference to FIG. 13. FIG. 13 is a flowchart showing the operation of an intruding object detection program for executing the intruding object detection method.
In an image input step 111, an input image 1201 having a width of 320 pixels, a height of 240 pixels, 8 bits per pixel, is acquired from an imaging device such as a TV camera, and then the process proceeds to step 112.
In the subtraction processing step 112, the difference between the input image 1201 and the background reference image 1202 is determined for each pixel and the difference image is acquired, followed by proceeding to step 113.
In the binarization processing step 113, the acquired difference image 1203 is processed based on a predetermined binarization threshold in such a manner that the pixels having a value not less than the intruding object detection threshold are defined as “255” and the pixels having a value less than the intruding object detection threshold are defined as “0”. In this way, the binarized image 1204 is acquired, followed by proceeding to step 114.
In the intruding object detection processing step 114, a cluster of pixels having the pixel value of “255” is detected from the binarized image 1204 by the labelling method, for example, and determined as an intruding object, followed by proceeding to step 115.
In the intruding object determining step 115, the process proceeds to step 116 in the case where an intruding object is detected in the intruding object detection processing step 114, while the process returns to the image input step 111 in the case where no intruding object is detected.
In the alarm/monitor display step 116, the processing result is displayed on a monitor 1113, for example, through an image I/F (the interface is hereinafter referred to as I/F) or an alarm lamp 1112 is turned on, for example, through an output I/F 1109.
In this way, according to the intruding object detection method using the subtraction method, the input image is compared with the reference background image to determine a difference for each pixel, and an area associated with a large difference is detected as an object. An object detection method using the subtraction method is described, for example, in JP-A-7-79429.
In this intruding object detection method, an intruding object is detected by comparing the difference between an input image and a reference background image for each pixel with a preset intruding object detection threshold.
In the case where the intruding object detection threshold is set to a small value, noise other than the intruding object (noise generated in the imaging device and noise superposed during the transmission of video signals) or objects other than the intruding object such as nodding trees are liable to be erroneously detected.
In the case where the intruding object detection threshold is set to a large value, on the other hand, those pixels making up an intruding object which have a brightness value approximate to those of the reference background image cannot be detected, and therefore an intruding object may be overlooked.
For this reason, the detection performance of an intruding object depends to a large measure on the intruding object detection threshold. The intruding object detection threshold, however, is required to be set in accordance with the luminance of the monitor view of field, the time zone and of the day (i.e., the time of the day) and the iris of the lens. This setting work is complicated and requires a high skill.
Further, in the case where an intruding object is detected using the subtraction method, the value to which the intruding object detection threshold is set is important. In the prior art, the setting work is burdensome as it is required to be carried out each time the luminance of the monitor field of view, the time zone of the day or the iris of the lens undergoes a change. Another disadvantage of the prior art lies in that it requires a high skill to set an intruding object detection threshold by automatically calculating the intruding object detection threshold whereby noises other than the intruding object can be removed from the input image acquired from the imaging device on the one hand and an intruding object detection threshold based on the detection of the reference intruding object contained in the imaging field of view on the other hand.